@inproceedings{2007,
  abstract     = {{Multisensor systems are susceptible to sensor ageing effects as well as to environmental changes. Due to these effects, the distribution of sensor measurements may change over time, which is referred to as sensor drift. A multisensor system which adapts to drift by self-monitoring is more durable, requires less manual maintenance, and provides information of higher quality. This contribution proposes an approach for detecting and adapting to sensor drift. The proposed detection algorithm determines the reliability of a sensor based on fuzzy pattern classifiers and a consistency measure. By this means, the inherent redundancy in multisensor systems is exploited to detect drift. Detected drift leads then to a retraining of the classifier on batched data guided by information fusion. The retraining incorporates the estimated magnitude of the drift. The proposed algorithms are evaluated in comparison with state-of-the-art methods in the scope of a publicly available dataset. It is shown that the drift detection algorithm yields results similar to the benchmark algorithm but is less computationally complex. Relearning with the drift-adapted approach results in more robust classifiers with regard to potential future drift.}},
  author       = {{Holst, Christoph-Alexander and Lohweg, Volker}},
  booktitle    = {{23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  keywords     = {{Multisensor systems, Temperature measurement, Current measurement, Redundancy, Pollution measurement, Detection algorithms}},
  location     = {{Torino, Italy}},
  title        = {{{A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion}}},
  doi          = {{10.1109/ETFA.2018.8502571}},
  year         = {{2018}},
}